The BioCyc collection of microbial genomes and metabolic pathways

Metabolic modelling of anaerobic amino acid uptake and storage by fermentative polyphosphate accumulating organisms

Existing enhanced biological phosphorus removal (EBPR) models do not fully describe the metabolism of fermentative polyphosphate accumulating organisms (fPAOs), particularly under mixed substrate conditions representative of fermentation-enhanced EBPR (F-EBPR) processes. This study presents a steady-state metabolic model integrating anaerobic amino acid (AA) fermentation and storage processes in fPAOs. The model identifies key metabolic interactions underlying fPAO metabolism, prioritising substrate accumulation over fermentation. This results in significant changes to ATP and reduction-oxidation (redox) flows as compared to when relying on previous AA fermentation models typically used to describe fPAO metabolism, with medium-chain-length (MCL) polyhydroxyalkanoate (PHA) formation and polyphosphate (polyP) consumption acting as important electron and energy management mechanisms, respectively. Succinate, rather than volatile fatty acids (VFAs), was identified as the more likely synergetic substrate between fermentative and conventional PAOs (cPAOs) under these conditions. Moreover, conditions favourable of VFA efflux by fPAOs may also favour a shift away from a polyP accumulation to a fermentation dominant metabolism. Further work is required to verify the role of MCL-PHA fractions, alongside the contribution of free intracellular AA accumulation as compared to polymers such as cyanophycin or polyglutamate on fPAO metabolism. This metabolic model provides a framework for better understanding the role of fPAOs and their interactions with cPAOs within EBPR processes, informing future modelling and optimisation of F-EBPR systems.

Higher order volatile fatty acid metabolism and atypical polyhydroxyalkanoate production in fermentation-enhanced biological phosphorus removal

Enhanced biological phosphorus removal (EBPR) is an established wastewater treatment process, but its wider implementation has been limited by factors like high temperature and low carbon availability. Fermentation-enhanced EBPR (F-EBPR) processes have shown promise in addressing these limitations, but the underlying mechanisms are not fully understood. This study investigates the metabolism of higher order (C4-5) volatile fatty acids (VFAs) in F-EBPR systems using a combination of carbon isotope labelling and shotgun metagenomic sequencing analyses. Results show that butyrate (HBu) uptake leads to the formation of both typical (C4-5) and atypical (C6+) polyhydroxyalkanoates (PHAs) through a combination ofβ-oxidation and standard condensation pathways, while the putative role of HBu oxidisers were identified relative to substrate composition in F-EBPR processes. Metagenomic analysis reveals the presence of genes required for higher order VFA metabolism in both polyphosphate accumulating organisms (PAOs) and glycogen accumulating organisms (GAOs). This study also highlights the limitations of current models in describing F-EBPR processes and emphasises the need for improved models that account for higher order VFA metabolism and microbial community dynamics.

Using supervised machine-learning approaches to understand abiotic stress tolerance and design resilient crops

Abiotic stresses such as drought, heat, cold, salinity and flooding significantly impact plant growth, development and productivity. As the planet has warmed, these abiotic stresses have increased in frequency and intensity, affecting the global food supply and making it imperative to develop stress-resilient crops. In the past 20 years, the development of omics technologies has contributed to the growth of datasets for plants grown under a wide range of abiotic environments. Integration of these rapidly growing data using machine-learning (ML) approaches can complement existing breeding efforts by providing insights into the mechanisms underlying plant responses to stressful conditions, which can be used to guide the design of resilient crops. In this review, we introduce ML approaches and provide examples of how researchers use these approaches to predict molecular activities, gene functions and genotype responses under stressful conditions. Finally, we consider the potential and challenges of using such approaches to enable the design of crops that are better suited to a changing environment.This article is part of the theme issue 'Crops under stress: can we mitigate the impacts of climate change on agriculture and launch the 'Resilience Revolution'?'.

Mycobacterium tuberculosis curli pili (MTP) and heparin-binding hemagglutinin adhesin (HBHA) facilitate regulation of central carbon metabolism, enhancement of ATP synthesis and cell wall biosynthesis

Functional 'omics' studies previously identified the M. tuberculosis surface located adhesins, heparin-binding hemagglutinin adhesin (HBHA) and curli pili (MTP) as significant potential targets for the design of tuberculosis (TB) point-of-care diagnostics, effective drugs, and vaccines. Little is known on the effect of these adhesins on the pathogen's transcriptome. The current study, via transcriptomics, elucidated whether the deletion of the single genes, hbhA and mtp, and double genes, hbhA-mtp, via specialised transduction, affected global bacterial gene expression. RNA sequencing of M. tuberculosis wild-type V9124 (WT), single and double deletion HBHA and MTP mutant strains were confirmed by reverse transcription quantitative polymerase chain reaction (RT-qPCR) on selected genes, and a functional bacterial ATP bioluminescence assay. The 43 significantly differentially expressed genes amongst the deletion mutants were functionally categorized into central carbon metabolism (CCM), cell wall biosynthesis and cell wall transport and processes. The increased expression of genes associated with ATP synthase and cell wall processes were confirmed by RT-qPCR. In the absence of the adhesins, a decreased ATP concentration was observed suggesting either increased utilization or alterations to the proton motive force (PMF) that resulted in a potential inhibition of ATP synthesis. Therefore, deletions of the mtp and hbhA genes were associated with significant perturbations in CCM regulation/function, and transport of proteins to the cell wall, indicating the significant contribution of these adhesins in fundamental processes contributing to TB pathogenesis. Thus, this study indicates that MTP and HBHA influence gene expression in M. tuberculosis and represent important targets for TB diagnostic/therapeutic interventions and should be investigated as vaccine and adjunctive therapies.

Mass-spectrometry based metabolomics: an overview of workflows, strategies, data analysis and applications

BACKGROUND

Metabolomics, a burgeoning field within systems biology, focuses on the comprehensive study of small molecules present in biological systems. Mass spectrometry (MS) has emerged as a powerful tool for metabolomic analysis due to its high sensitivity, resolution, and ability to characterize a wide range of metabolites thus offering deep insights into the metabolic profiles of living systems.

AIM OF REVIEW

This review provides an overview of the methodologies, workflows, strategies, data analysis techniques, and applications associated with mass spectrometry-based metabolomics.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We discuss workflows, key strategies, experimental procedures, data analysis techniques, and diverse applications of metabolomics in various research domains. Nuances of sample preparation, metabolite extraction, separation using chromatographic techniques, mass spectrometry analysis, and data processing are elaborated. Moreover, standards, quality controls, metabolite annotation, software for statistical and pathway analysis are also covered. In conclusion, this review aims to facilitate the understanding and adoption of mass spectrometry-based metabolomics by newcomers and researchers alike by providing a foundational understanding and insights into the current state and future directions of this dynamic field.

Model of metabolism and gene expression predicts proteome allocation in Pseudomonas putida

The genome-scale model of metabolism and gene expression (ME-model) for Pseudomonas putida KT2440, iPpu1676-ME, provides a comprehensive representation of biosynthetic costs and proteome allocation. Compared to a metabolic-only model, iPpu1676-ME significantly expands on gene expression, macromolecular assembly, and cofactor utilization, enabling accurate growth predictions without additional constraints. Multi-omics analysis using RNA sequencing and ribosomal profiling data revealed translational prioritization in P. putida, with core pathways, such as nicotinamide biosynthesis and queuosine metabolism, exhibiting higher translational efficiency, while secondary pathways displayed lower priority. Notably, the ME-model significantly outperformed the M-model in alignment with multi-omics data, thereby validating its predictive capacity. Thus, iPpu1676-ME offers valuable insights into P. putida's proteome allocation and presents a powerful tool for understanding resource allocation in this industrially relevant microorganism.

Genome-scale metabolic model of Staphylococcus epidermidis ATCC 12228 matches in vitro conditions

Staphylococcus epidermidis, a commensal bacterium inhabiting collagen-rich areas like human skin, has gained significance due to its probiotic potential in the nasal microbiome and as a leading cause of nosocomial infections. While infrequently leading to severe illnesses, S. epidermidis exerts a significant influence, particularly in its close association with implant-related infections and its role as a classic opportunistic biofilm former. Understanding its opportunistic nature is crucial for developing novel therapeutic strategies, addressing both its beneficial and pathogenic aspects, and alleviating the burdens it imposes on patients and healthcare systems. Here, we employ genome-scale metabolic modeling as a powerful tool to elucidate the metabolic capabilities of S. epidermidis. We created a comprehensive computational resource for understanding the organism's growth conditions within diverse habitats by reconstructing and analyzing a manually curated and experimentally validated metabolic model. The final network, iSep23, incorporates 1,415 reactions, 1,051 metabolites, and 705 genes, adhering to established community standards and modeling guidelines. Benchmarking with the Metabolic Model Testing suite yields a high score, indicating the model's remarkable semantic quality. Following the findable, accessible, interoperable, and reusable (FAIR) data principles, iSep23 becomes a valuable and publicly accessible asset for subsequent studies. Growth simulations and carbon source utilization predictions align with experimental results, showcasing the model's predictive power. Ultimately, this work provides a robust foundation for future research aimed at both exploiting the probiotic potential and mitigating the pathogenic risks posed by S. epidermidis.

IMPORTANCE

Staphylococcus epidermidis, a bacterium commonly found on human skin, has shown probiotic effects in the nasal microbiome and is a notable causative agent of hospital-acquired infections. While these infections are typically non-life-threatening, their economic impact is considerable, with annual costs reaching billions of dollars in the United States. To better understand its opportunistic nature, we employed genome-scale metabolic modeling to construct a detailed network of S. epidermidis's metabolic capabilities. This model, comprising over a thousand reactions, metabolites, and genes, adheres to established standards and demonstrates solid benchmarking performance. Following the findable, accessible, interoperable, and reusable (FAIR) data principles, the model provides a valuable resource for future research. Growth simulations and predictions closely match experimental data, underscoring the model's predictive accuracy. Overall, this work lays a solid foundation for future studies aimed at leveraging the beneficial properties of S. epidermidis while mitigating its pathogenic potential.

Usnic acid impacts energy production and iron metabolism in Mycobacterium tuberculosis H37Rv

Mycobacterium tuberculosis has developed a wide array of response mechanisms to various stress factors. Usnic acid has been demonstrated to be a potent antimycobacterial agent that induces stress responses and growth inhibition in many mycobacterial species. Previous studies have shown that it alters the expression of stress-responsive sigma factors, as well as the metabolites and lipid profile in M. tuberculosis H37Ra. This study was designed to examine potential differences in the strain-specific susceptibility of the virulent H37Rv strain to usnic acid. By combining lipidomic and transcriptomic analyses, we uncovered the impact of usnic acid on bacterial metabolism. The observed downregulation of key lipid classes suggested reduced metabolic activity. The simultaneous elevation of mycobactins-siderophores used by members of the genus Mycobacterium to transport free extracellular iron ions into the cytoplasm-indicated the involvement of iron in the stress response generated by usnic acid. The repressed tricarboxylic acid (TCA) cycle and oxidative phosphorylation were compensated by the upregulation of alternative energy production pathways, such as cytochrome P450 and the ferredoxin reductase system. This indicates that mycobacteria may switch to alternative electron transport mechanisms under usnic acid stress using iron-sulfur clusters to generate energy. From a therapeutic perspective, the study highlights iron metabolism as an essential drug target in mycobacteria. Simultaneously, the results confirm the strain-specific metabolic response of sister strains against the same stressing agent.

IMPORTANCE

A previous study on the influence of usnic acid on the avirulent H37Ra strain revealed that the early bacterial response was associated with redox homeostasis, lipid synthesis, and nucleic acid repair. The response of bacteria to antimicrobials is specific to each species and strain. Given the genetic and phenotypic differences between the avirulent H37Ra strain and the virulent H37Rv strain, we combined lipidomics and global transcriptomics to uncover the mechanism of action of usnic acid against H37Rv. The study identified strain-specific differences between the virulent H37Rv and avirulent H37Ra. The H37Ra strain exhibited increased metabolic activity, while the H37Rv strain showed a reduction in basic metabolic processes and activated alternative iron-dependent energy production. These differences highlight the varying susceptibility of sister strains within the same species to the same antibacterial agent.

Peroxisome Proliferator-Activated Receptors (PPARs) May Mediate the Neuroactive Effects of Probiotic Metabolites: An In Silico Approach

It is well established that the gut-brain axis (GBA) is a bidirectional communication between the gut and the brain. This axis, critical in maintaining overall homeostasis, is regulated at the neuronal, endocrine, and immunological levels, all of which may be influenced by the gut microbiota (GM). Therefore, dysbiosis or disruption in the GM may have serious consequences including neuroinflammation due to overactivation of the immune system. Strategies to reestablish GM integrity via use of probiotics are being pursued as novel therapeutic intervention in a variety of central and peripheral diseases. The mechanisms leading to dysbiosis or efficacy of probiotics, however, are not fully evident. Here, we performed computational analysis on two major probiotics, namely Lactobacillus Lacticaseibacillus rhamnosus GG (formerly named Lactobacillus rhamnosus, L. rhamnosus GG) and Bifidobacterium animalis spp. lactis (B. lactis or B. animalis) to not only shed some light on their mechanism(s) of action but also to identify potential molecular targets for novel probiotics. Using the PubMed web page and BioCyc Database Collection platform we specifically analyzed proteins affected by metabolites of these bacteria. Our results indicate that peroxisome proliferator-activated receptors (PPARs), nuclear receptor proteins that are involved in regulation of inflammation are key mediators of the neuroactive effect of probiotics.

Unconventional mRNA processing and degradation pathways for the polycistronic yrzI (spyTA) mRNA in Bacillus subtilis

The ribosome-associated endoribonuclease Rae1 cleaves the Bacillus subtilis yrzI operon mRNA in a translation-dependent manner. This operon encodes up to four small peptides, S1027, YrzI, S1025, and S1024, whose functions are unknown. Here, we identified the function of YrzI and S1025 and deciphered the degradation pathways of the yrzI polycistronic mRNA. We show that YrzI is toxic at high concentrations, but co-expression with S1025 abolishes its toxicity, and that, in the absence of Rae1, S1025 is the major antidote to the YzI toxin. We show that a highly stable mRNA species containing the YrzI and S1025 open reading frames results from endoribonucleolytic cleavage upstream of yrzI followed by the arrest of 5'-exoribonucleolytic processing by ribosomes bound to its exceptionally strong Shine-Dalgarno sequence. Degradation of this mRNA requires either translation-dependent cleavage within S1025 by Rae1 or direct attack from the structured 3'-end by 3'-exoribonucleases. Neither pathway is common for a B. subtilis mRNA.

Production of Vanillin From Ferulic Acid by Pseudomonas putida KT2440 Using Metabolic Engineering and In Situ Product Recovery

Vanillin is the most in-demand flavouring compound in the world and because vanillin extracted from vanilla pods cannot meet the global demand, most vanillin on the market today is chemically synthesised. Increasing demands by consumers for natural ingredients have inspired efforts to develop vanillin derived from microbial sources. These efforts have been challenged by low titers, likely caused by the toxicity of vanillin to most microbial biocatalysts. In this study, we engineered a Pseudomonas putida KT2440-derived strain that accumulated vanillin from ferulic acid to 0.64 g/L. To increase the overall titre, we applied a hydrophobic polystyrene-based resin to vanillin-accumulating cultures, which enabled an increase in total vanillin recovery to an apparent titre of 3.35 g/L. This study demonstrates that P. putida can accumulate vanillin from ferulic acid to higher titers when vanillin is removed from the cultivation medium, mitigating its toxicity.

Structural and functional analysis of Pseudomonas aeruginosa PelA provides insight into the modification of the Pel exopolysaccharide

A major biofilm matrix determinant of Pseudomonas aeruginosa is the partially deacetylated α-1,4 linked N-acetylgalactosamine polymer, Pel. After synthesis and transport of the GalNAc polysaccharide across the inner membrane, PelA partially deacetylates and hydrolyzes Pel before its export out of the cell via PelB. While the Pel modification and export proteins are known to interact in the periplasm, it is unclear how the interaction of PelA and PelB coordinates these processes. To determine how PelA modifies the polymer, we determined its structure to 2.1 Å and found a unique arrangement of four distinct domains. We have shown previously that the hydrolase domain exhibits endo-α-1,4-N-acetylgalactosaminidase activity. Characterization of the deacetylase domain revealed that PelA is the founding member of a new carbohydrate esterase family, CE21. Further, we found that the PelAB interaction enhances the deacetylation of N-acetylgalactosamine oligosaccharides. Using the PelA structure in conjunction with AlphaFold2 modeling of the PelAB complex, we propose a model wherein PelB guides Pel to the deacetylase domain of PelA and subsequently to the porin domain of PelB for export. Perturbation or loss of the PelAB interaction would result in less efficient deacetylation and potentially increased Pel hydrolysis. In PelA homologs across many phyla, the predicted structure and active sites are conserved, suggesting a common modification mechanism in Gram-negative bacterial species containing a functional pel operon.

Complex system modeling reveals oxalate homeostasis is driven by diverse oxalate-degrading bacteria

Decades of research have made clear that host-associated microbiomes touch all facets of health. However, effective therapies that target the microbiome have been elusive given its inherent complexity. Here, we experimentally examined diet-microbe-host interactions through a complex systems framework, centered on dietary oxalate. Using multiple, independent molecular, rodent, and in vitro experimental models, we found that microbiome composition influenced multiple oxalate-microbe-host interfaces. Importantly, the administration of the oxalate-degrading specialist, Oxalobacter formigenes, was only effective against a poor oxalate-degrading microbiota background and gives critical new insights into why clinical intervention trials with this species exhibit variable outcomes. Data suggest that, while heterogeneity in the microbiome impacts multiple diet-host-microbe interfaces, metabolic redundancy among diverse microorganisms in specific diet-microbe axes is a critical variable that may impact the efficacy of bacteriotherapies, which can help guide patient and probiotic selection criteria in probiotic clinical trials.

Conserved genetic basis for microbial colonization of the gut

Despite the fundamental importance of gut microbes, the genetic basis of their colonization remains largely unexplored. Here, by applying cross-species genotype-habitat association at the tree-of-life scale, we identify conserved microbial gene modules associated with gut colonization. Across thousands of species, we discovered 79 taxonomically diverse putative colonization factors organized into operonic and non-operonic modules. They include previously characterized colonization pathways such as autoinducer-2 biosynthesis and novel processes including tRNA modification and translation. In vivo functional validation revealed YigZ (IMPACT family) and tRNA hydroxylation protein-P (TrhP) are required for E. coli intestinal colonization. Overexpressing YigZ alone is sufficient to enhance colonization of the poorly colonizing MG1655 E. coli by >100-fold. Moreover, natural allelic variations in YigZ impact inter-strain colonization efficiency. Our findings highlight the power of large-scale comparative genomics in revealing the genetic basis of microbial adaptations. These broadly conserved colonization factors may prove critical for understanding gastrointestinal (GI) dysbiosis and developing therapeutics.

Collaborative metabolic curation of an emerging model marine bacterium, Alteromonas macleodii ATCC 27126

Inferring the metabolic capabilities of an organism from its genome is a challenging process, relying on computationally-derived or manually curated metabolic networks. Manual curation can correct mistakes in the draft network and add missing reactions based on the literature, but requires significant expertise and is often the bottleneck for high-quality metabolic reconstructions. Here, we present a synopsis of a community curation workshop for the model marine bacterium Alteromonas macleodii ATCC 27126 and its genome database in BioCyc, focusing on pathways for utilizing organic carbon and nitrogen sources. Due to the scarcity of biochemical information or gene knock-outs, the curation process relied primarily on published growth phenotypes and bioinformatic analyses, including comparisons with related Alteromonas strains. We report full pathways for the utilization of the algal polysaccharides alginate and pectin in contrast to inconclusive evidence for one-carbon metabolism and mixed acid fermentation, in accordance with the lack of growth on methanol and formate. Pathways for amino acid degradation are ubiquitous across Alteromonas macleodii strains, yet enzymes in the pathways for the degradation of threonine, tryptophan and tyrosine were not identified. Nucleotide degradation pathways are also partial in ATCC 27126. We postulate that demonstrated growth on nitrate as sole nitrogen source proceeds via a nitrate reductase pathway that is a hybrid of known pathways. Our evidence highlights the value of joint and interactive curation efforts, but also shows major knowledge gaps regarding Alteromonas metabolism. The manually-curated metabolic reconstruction is available as a "Tier-2" database on BioCyc.

ArtSymbioCyc, a metabolic network database collection dedicated to arthropod symbioses: a case study, the tripartite cooperation in Sipha maydis

Most arthropods live in close association with bacteria. The genomes of associated partners have co-evolved, creating situations of interdependence that are complex to decipher despite the availability of their complete sequences. We developed ArtSymbioCyc, a metabolism-oriented database collection gathering genomic resources for arthropods and their associated bacteria. ArtSymbioCyc uses the powerful tools of the BioCyc community to produce high-quality annotations and to analyze and compare metabolic networks on a genome-wide scale. We used ArtSymbioCyc to study the case of the tripartite symbiosis of the cereal aphid Sipha maydis focusing on amino acid and vitamin metabolisms, as these compounds are known to be important in this strictly phloemophagous insect. We showed that the metabolic pathways of the insect host and its two obligate bacterial associates are interdependent and specialized in the exploitation of Poaceae phloem, particularly for the biosynthesis of sulfur-containing amino acids and most vitamins. This demonstrates that ArtSymbioCyc does not only reveal the individual metabolic capacities of each partner and their respective contributions to the holobiont they constitute but also allows to predict the essential inputs that must come from host nutrition.IMPORTANCEThe evolution has driven the emergence of complex arthropod-microbe symbiotic systems, whose metabolic integration is difficult to unravel. With its user-friendly interface, ArtSymbioCyc (https://artsymbiocyc.cycadsys.org) eases and speeds up the analysis of metabolic networks by enabling precise inference of compound exchanges between associated partners and helps unveil the adaptive potential of arthropods in contexts such as conservation or agricultural control.

PathwayMind: An innovative tool and database for pathway perturbation analysis, uncovering critical pathways for drugs and target protein sets

Pathway enrichment analysis is a valuable tool for researchers aiming to understand the mechanisms underlying any specific drug or disease associated gene lists derived from biological assays or large-scale genome (omics) experiments. By employing this method, researchers can pinpoint biological pathways that exhibit a higher level of enrichment for a given set of genes than would be anticipated by random chance. These biological pathways play a crucial role in understanding the disease pathophysiology, therapeutic strategy, polypharmacological effects, synergistic mechanisms, and target engagement. However many of the available tools do not fulfill this requirement as most of the available tools consider a flat hierarchy of protein involvement in the pathway and do not consider topological information or importance of molecules in the pathway. Here we propose a novel method to enrich the molecular pathways and prioritize them based on their importance and crucial role in the biological function using the graph and evidence-based approach and customized datasets called PathwayMind. It includes 2648 pathways, 4539 biological events, 2465847 protein-protein interactions and 124717 gene-to-pathway relationships and the role of 3510 unique initial genes in 11,992 molecular pathways. The current manuscript comprises three major steps: The first step is about the data extraction and datasets creation for pathway enrichment, and the second steps comprises pathway perturbation analysis to identify most perturbed biological pathways and the third steps includes validation of this approach along with standalone tools and visualization algorithms which disclose the molecular involvement and improve the interpretability of the results. The end-to-end pathway analysis can be performed in a few minutes to provide complete insights of your target or drug of interest. The current PathwayMind tool and its datasets could be very useful for the molecular scientists and system biologists who are interested to understand the therapeutic effects of their drugs or understanding the involvement of biological pathways for specific gene sets which does not require any prior bioinformatics training.

Hyperspectral reporters for long-distance and wide-area detection of gene expression in living bacteria

Genetically encoded reporters are suitable for short-distance imaging in the laboratory but not for scanning wide outdoor areas from a distance. Here we introduce hyperspectral reporters (HSRs) designed for hyperspectral imaging cameras that are commonly mounted on unmanned aerial vehicles and satellites. HSR genes encode enzymes that produce a molecule with a unique absorption signature that can be reliably distinguished in hyperspectral images. Quantum mechanical simulations of 20,170 metabolites identified candidate HSRs, leading to the selection of biliverdin IXα and bacteriochlorophyll a for their distinct absorption spectra and biosynthetic feasibility. These genes were integrated into chemical sensor circuits in soil (Pseudomonas putida) and aquatic (Rubrivivax gelatinosus) bacteria. The bacteria were detectable outdoors under ambient light from up to 90 m in a single 4,000-m2 hyperspectral image taken using fixed and unmanned aerial vehicle-mounted cameras. The dose-response functions of the chemical sensors were measured remotely. HSRs enable large-scale studies and applications in ecology, agriculture, environmental monitoring, forensics and defense.

High-resolution mapping of sigma factor DNA-binding sequences using artificial promoters, RNA aptamers, and deep sequencing

The variable sigma (σ) subunit of the bacterial RNA polymerase holoenzyme determines promoter specificity and facilitates open complex formation during transcription initiation. Understanding σ-factor binding sequences is therefore crucial for deciphering bacterial gene regulation. Here, we present a data-driven high-throughput approach that utilizes an extensive library of 1.54 million DNA templates providing artificial promoters and 5' untranslated region sequences for σ-factor DNA-binding motif discovery. This method combines the generation of extensive DNA libraries, in vitro transcription, RNA aptamer, and deep DNA and RNA sequencing. It allows direct assessment of promoter activity, identification of transcription start sites, and quantification of promoter strength based on mRNA production levels. We applied this approach to map σ54 DNA-binding sequences in Pseudomonas putida. Deep sequencing of the enriched RNA pool revealed 64 966 distinct σ54 binding motifs, significantly expanding the known repertoire. This data-driven approach surpasses traditional methods by directly evaluating promoter function and avoiding selection bias based solely on binding affinity. This comprehensive dataset enhances our understanding of σ-factor binding sequences and their regulatory roles, opening avenues for new research in biology and biotechnology.

Are Bacterial Processes Dependent on Global Ribosome Pausing Affected by tRNA Modification Defects?

By integrating a literature review with transcriptomic, proteomic, and phenotypic data from two model bacteria, Escherichia coli and Vibrio cholerae, we put forward the hypothesis that defects in tRNA modification broadly impact processes that are evolutionarily tuned to be sensitive to translation speed. These include the translation of regulatory proteins associated with motility, iron homeostasis, and leader peptide-driven attenuation mechanisms. Some of these translation speed-dependent processes are influenced by the absence of a single modification, while others are affected by the absence of multiple modifications. Although further experiments are needed to clarify the mechanisms involved in each case, this work provides a foundational framework to guide future research.

Machine Learning-Enabled Drug-Induced Toxicity Prediction

Unexpected toxicity has become a significant obstacle to drug candidate development, accounting for 30% of drug discovery failures. Traditional toxicity assessment through animal testing is costly and time-consuming. Big data and artificial intelligence (AI), especially machine learning (ML), are robustly contributing to innovation and progress in toxicology research. However, the optimal AI model for different types of toxicity usually varies, making it essential to conduct comparative analyses of AI methods across toxicity domains. The diverse data sources also pose challenges for researchers focusing on specific toxicity studies. In this review, 10 categories of drug-induced toxicity is examined, summarizing the characteristics and applicable ML models, including both predictive and interpretable algorithms, striking a balance between breadth and depth. Key databases and tools used in toxicity prediction are also highlighted, including toxicology, chemical, multi-omics, and benchmark databases, organized by their focus and function to clarify their roles in drug-induced toxicity prediction. Finally, strategies to turn challenges into opportunities are analyzed and discussed. This review may provide researchers with a valuable reference for understanding and utilizing the available resources to bridge prediction and mechanistic insights, and further advance the application of ML in drugs-induced toxicity prediction.

Influence of Amino Acids on Quorum Sensing-Related Pathways in Pseudomonas aeruginosa PAO1: Insights from the GEM iJD1249

BACKGROUND/OBJECTIVES

Amino acids (AAs) play a critical role in diseases such as cystic fibrosis where Pseudomonas aeruginosa PAO1 adapts its metabolism in response to host-derived nutrients. The adaptation influences virulence and complicates antibiotic treatment mainly for the antimicrobial resistance context. D- and L-AAs have been analyzed for their impact on quorum sensing (QS), a mechanism that regulates virulence factors. This research aimed to reconstruct the genome-scale metabolic model (GEM) of P. aeruginosa PAO1 to investigate the metabolic roles of D- and L-AAs in QS-related pathways.

METHODS

The updated GEM, iJD1249, was reconstructed by using protocols to integrate data from previous models and refined with well-standardized in silico media (LB, M9, and SCFM) to improve flux balance analysis accuracy. The model was used to explore the metabolic impact of D-Met, D-Ala, D-Glu, D-Ser, L-His, L-Glu, L-Arg, and L-Ornithine (L-Orn) at 5 and 50 mM in QS-related pathways, focusing on the effects on bacterial growth and carbon flux distributions.

RESULTS

Among the tested AAs, D-Met was the only one that did not enhance the growth rate of P. aeruginosa PAO1, while L-Arg and L-Orn increased fluxes in the L-methionine biosynthesis pathway, influencing the metH gene. These findings suggest a differential metabolic role for D-and L-AAs in QS-related pathways.

CONCLUSIONS

Our results shed some light on the metabolic impact of AAs on QS-related pathways and their potential role in P. aeruginosa virulence. Future studies should assess D-Met as a potential adjuvant in antimicrobial strategies, optimizing the concentration in combination with antibiotics to maximize its therapeutic effectiveness.

Gene network centrality analysis identifies key regulators coordinating day-night metabolic transitions in Synechococcus elongatus PCC 7942 despite limited accuracy in predicting direct regulator-gene interactions

Synechococcus elongatus PCC 7942 is a model organism for studying circadian regulation and bioproduction, where precise temporal control of metabolism significantly impacts photosynthetic efficiency and CO2-to-bioproduct conversion. Despite extensive research on core clock components, our understanding of the broader regulatory network orchestrating genome-wide metabolic transitions remains incomplete. We address this gap by applying machine learning tools and network analysis to investigate the transcriptional architecture governing circadian-controlled gene expression. While our approach showed moderate accuracy in predicting individual transcription factor-gene interactions - a common challenge with real expression data - network-level topological analysis successfully revealed the organizational principles of circadian regulation. Our analysis identified distinct regulatory modules coordinating day-night metabolic transitions, with photosynthesis and carbon/nitrogen metabolism controlled by day-phase regulators, while nighttime modules orchestrate glycogen mobilization and redox metabolism. Through network centrality analysis, we identified potentially significant but previously understudied transcriptional regulators: HimA as a putative DNA architecture regulator, and TetR and SrrB as potential coordinators of nighttime metabolism, working alongside established global regulators RpaA and RpaB. This work demonstrates how network-level analysis can extract biologically meaningful insights despite limitations in predicting direct regulatory interactions. The regulatory principles uncovered here advance our understanding of how cyanobacteria coordinate complex metabolic transitions and may inform metabolic engineering strategies for enhanced photosynthetic bioproduction from CO2.

Machine learning uncovers the transcriptional regulatory network for the production host Streptomyces albidoflavus

Streptomyces albidoflavus is a widely used strain for natural product discovery and production through heterologous biosynthetic gene clusters (BGCs). However, the transcriptional regulatory network (TRN) and its impact on secondary metabolism remain poorly understood. Here, we characterize the TRN using independent component analysis on 218 RNA sequencing (RNA-seq) transcriptomes across 88 unique growth conditions. We identify 78 independently modulated sets of genes (iModulons) that quantitatively describe the TRN across diverse conditions. Our analyses reveal (1) TRN adaptation to different growth conditions, (2) conserved and unique characteristics of the TRN across diverse lineages, (3) transcriptional activation of several endogenous BGCs, including surugamide, minimycin, and paulomycin, and (4) inferred functions of 40% of uncharacterized genes in the S. albidoflavus genome. These findings provide a comprehensive and quantitative understanding of the S. albidoflavus TRN, offering a knowledge base for further exploration and experimental validation.

Blood metabolites mediate causal inference studies on the effect of gut microbiota on the risk of vascular calcification

BACKGROUND

Emerging evidence indicates a notable connection between gut microbiota and Vascular Calcification (VC). Gut microbiota influences various disease processes through host metabolic pathways; however, the causative link between gut microbiota and VC, along with the potential mediating role of metabolites, is still not well understood.

METHODS

We leveraged data from the largest Genome-Wide Association Studies (GWAS) concerning gut microbiota, blood metabolites, and VC. To explore the causal relationships among these variables, we conducted two-sample bidirectional Mendelian Randomization (MR) analyses. Furthermore, mediation analyses were conducted to determine if metabolites act as an intermediary in the impact of gut microbiota on VC. In addition, we recruited CKD patients for mass spectrometry and CT examination, and performed a correlation analysis between the expression of blood metabolites and VC score. Finally, we experimentally validated the effects of intermediate metabolites on VC.

RESULTS

We identified 19 positive gut microbiota species and 52 positive blood metabolites with causal effects on VC. Additionally, the onset of VC was found to induce changes in the abundance of 24 gut microbiota species and 56 metabolites. Further analyses revealed that up to 13 positive gut microbiota species regulate the expression of 20 positive metabolites. Mediation analysis suggests that the gut microbiota g_KLE1615 promotes VC by downregulating the methionine-to-phosphate ratio. Mass spectrometry results indicate that over half of the metabolites identified through MR analysis show altered expression during CKD progression. Among them, 7 metabolites were significantly associated with the progression of VC. Further in vitro experiments confirmed the inhibitory effect of the intermediate metabolite methionine on VC.

CONCLUSION

Gut microbiota and blood metabolites are causally linked to VC. These findings provide a theoretical basis for microbiome- and metabolome-based therapeutic strategies for targeting VC and enhances our comprehension of the gut-vascular axis.

A small periplasmic protein governs broad physiological adaptations in Vibrio cholerae via regulation of the DbfRS two-component system

Two-component signaling pathways allow bacteria to sense and respond to environmental changes, yet the sensory mechanisms of many remain poorly understood. In the pathogen Vibrio cholerae, the DbfRS two-component system controls the biofilm lifecycle, a critical process for environmental persistence and host colonization. Here, we identified DbfQ, a small periplasmic protein encoded adjacent to dbfRS, as a direct modulator of pathway activity. DbfQ directly binds the sensory domain of the histidine kinase DbfS, shifting it toward phosphatase activity and promoting biofilm dispersal. In contrast, outer membrane perturbations, caused by mutations in lipopolysaccharide biosynthesis genes or membrane-damaging antimicrobials, activate phosphorylation of the response regulator DbfR. Transcriptomic analyses reveal that DbfR phosphorylation leads to broad transcriptional changes spanning genes involved in biofilm formation, central metabolism, peptidoglycan synthesis, and cellular stress responses. Constitutive DbfR phosphorylation imposes severe fitness costs in an infection model, highlighting this pathway as a potential target for anti-infective therapeutics. We find that dbfQRS-like genetic modules are widely present across bacterial phyla, underscoring their broad relevance in bacterial physiology. Collectively, these findings establish DbfQ as a new class of periplasmic regulator that influences two-component signaling and bacterial adaptation.

Interrogating mediators of single-cell transcriptional changes in the acute damaged cerebral cortex: Insights into endothelial-astrocyte interactions

Traumatic brain injury (TBI) induces complex cellular and molecular changes, challenging recovery and therapeutic development. Although molecular pathways have been implicated in TBI pathology, the cellular specificity of these mechanisms remains underexplored. Here, we investigate the role of endothelial cell (EC) EphA4, a receptor tyrosine kinase receptor involved in axonal guidance, in modulating cell-specific transcriptomic changes within the damaged cerebral cortex. Utilizing single-cell RNA sequencing (scRNA-seq) in an experimental TBI model, we mapped transcriptional changes across various cell types, with a focus on astrocytes and ECs. Our analysis reveals that EC-specific knockout (KO) of EphA4 triggers significant alterations in astrocyte gene expression and shifts predominate subclusters. We identified six distinct astrocyte clusters (C0-C5) in the damaged cortex including as C0-Mobp/Plp1+; C1-Slc1a3/Clu+; C2-Hbb-bs/Hba-a1/Ndrg2+; C3-GFAP/Lcn2+; C4-Gli3/Mertk+, and C5-Cox8a+. We validate a new Sox9+ cluster expressing Mertk and Gas, which mediates efferocytosis to facilitate apoptotic cell clearance and anti-inflammatory responses. Transcriptomic and CellChat analyses of EC-KO cells highlights upregulation of neuroprotective pathways, including increased amyloid precursor protein (APP) and Gas6. Key pathways predicted to be modulated in astrocytes from EC-KO mice include oxidative phosphorylation and FOXO signaling, mitochondrial dysfunction and ephrin B signaling. Concurrently, metabolic and signaling pathways in endothelial cells-such as ceramide and sphingosine phosphate metabolism and NGF-stimulated transcription-indicate an adaptive response to a metabolically demanding post-injury hypoxic environment. These findings elucidate potential interplay between astrocytic and endothelial responses as well as transcriptional networks underlying cortical tissue damage.

Strategies to include prior knowledge in omics analysis with deep neural networks

High-throughput molecular profiling technologies have revolutionized molecular biology research in the past decades. One important use of molecular data is to make predictions of phenotypes and other features of the organisms using machine learning algorithms. Deep learning models have become increasingly popular for this task due to their ability to learn complex non-linear patterns. Applying deep learning to molecular profiles, however, is challenging due to the very high dimensionality of the data and relatively small sample sizes, causing models to overfit. A solution is to incorporate biological prior knowledge to guide the learning algorithm for processing the functionally related input together. This helps regularize the models and improve their generalizability and interpretability. Here, we describe three major strategies proposed to use prior knowledge in deep learning models to make predictions based on molecular profiles. We review the related deep learning architectures, including the major ideas in relatively new graph neural networks.

Insights into Functions of Universal Stress Proteins Encoded by Genomes of Gastric Cancer Pathogen Helicobacter pylori and Related Bacteria

The genes that encode the universal stress protein (USP) family domain (pfam00582) aid the survival of bacteria in specific host or habitat-induced stress conditions. Genome sequencing revealed that the genome of Helicobacter pylori, a gastric cancer pathogen, typically contains one USP gene, while related helicobacters have one or two distinct USP genes. However, insights into the functions of Helicobacteraceae (Helicobacter and Wolinella) USP genes are still limited to inferences from large-scale genome sequencing. Thus, we have combined bioinformatics and visual analytics approaches to conduct a more comprehensive data investigation of a set of 1045 universal stress protein sequences encoded in 1014 genomes including 785 Helicobacter pylori genomes. The study generated a representative set of 183 USP sequences consisting of 180 Helicobacter sequences, two Wolinella succinogenes sequences, and a sequence from a related campylobacteria. We used the amino acid residues and positions of the 12 possible functional sites in 1030 sequences to identify 25 functional sites patterns for guiding studies on functional interactions of Helicobacteraceae USPs with ATP and other molecules. Genomic context searches and analysis identified USP genes of gastric and enterohepatic helicobacters that are adjacent or in operons with genes for proteins responsive to DNA-damaging oxidative stress (ATP-dependent proteases: ClpS and ClpA); and DNA uptake proteins (natural competence for transformation proteins: ComB6, ComB7, ComB8, ComB9, ComB10, ComBE, and conjugative transfer signal peptidase TraF). Since transcriptomic evidence indicates that oxidative stress and the presence of virulence-associated genes regulate the transcription of H. pylori USP gene, we recommend further research on Helicobacter USP genes and their neighboring genes in oxidative stress response and virulence of helicobacters. To facilitate the reuse of data and research, we produced interactive analytics resources of a dataset composed of values for variables including phylogeography of H. pylori strains, protein sequence features, and gene neighborhood.

Identification of two glycosyltransferases required for synthesis of membrane glycolipids in Clostridioides difficile

Clostridioides difficile infections cause over 12,000 deaths and an estimated one billion dollars in healthcare costs annually in the United States. The cell membrane is an essential structure that is important for protection from the extracellular environment, signal transduction, and transport of nutrients. The polar membrane lipids of C. difficile are ~50% glycolipids, a higher percentage than most other organisms. The glycolipids of C. difficile consist of monohexosyldiradylglycerol (MHDRG) (~14%), dihexosyldiradylglycerol (DHDRG) (~15%), trihexosyldiradylglycerol (THDRG) (~5%), and a unique glycolipid aminohexosyl-hexosyldiradylglycerol (HNHDRG) (~16%). Previously, we found that HexSDF are required for the synthesis of HNHDRG. The enzymes required for the synthesis of MHDRG, DHDRG, and THDRG are not known. In this study, we identified the glycosyltransferases UgtA (CDR20291_0008), which is required for the synthesis of all glycolipids, and UgtB (CDR20291_1186), which is required for the synthesis of DHDRG and THDRG. We propose a model where UgtA synthesizes only MHDRG, HexSDF synthesize HNHDRG from MHDRG, and UgtB synthesizes DHDRG and potentially THDRG from MHDRG. We also report that glycolipids are important for critical cell functions, including sporulation, cell size and morphology, maintaining membrane fluidity, colony morphology, and resistance to some membrane-targeting antimicrobials.

IMPORTANCE

Clostridioides difficile infections are the leading cause of healthcare-associated diarrhea. C. difficile poses a risk to public health due to its ability to form spores and cause recurrent infections. Glycolipids make up ~50% of the polar lipids in the C. difficile membrane, a higher percentage than other common pathogens and include a unique glycolipid not present in other organisms. Here, we identify glycosyltransferases required for the synthesis of glycolipids in C. difficile and demonstrate the important role glycolipids play in C. difficile physiology.

Cyanobacterial circadian regulation enhances bioproduction under subjective nighttime through rewiring of carbon partitioning dynamics, redox balance orchestration, and cell cycle modulation

BACKGROUND

The industrial feasibility of photosynthetic bioproduction using cyanobacterial platforms remains challenging due to insufficient yields, particularly due to competition between product formation and cellular carbon demands across different temporal phases of growth. This study investigates how circadian clock regulation impacts carbon partitioning between storage, growth, and product synthesis in Synechococcus elongatus PCC 7942, and provides insights that suggest potential strategies for enhanced bioproduction.

RESULTS

After entrainment to light-dark cycles, PCC 7942 cultures transitioned to constant light revealed distinct temporal patterns in sucrose production, exhibiting three-fold higher productivity during subjective night compared to subjective day despite moderate down-regulation of genes from the photosynthetic apparatus. This enhanced productivity coincided with reduced glycogen accumulation and halted cell division at subjective night time, suggesting temporal separation of competing processes. Transcriptome analysis revealed coordinated circadian clock-driven adjustment of the cell cycle and rewiring of energy and carbon metabolism, with over 300 genes showing differential expression across four time points. The subjective night was characterized by altered expression of cell division-related genes and reduced expression of genes involved in glycogen synthesis, while showing upregulation of glycogen degradation pathways, alternative electron flow components, the pentose phosphate pathway, and oxidative decarboxylation of pyruvate. These molecular changes created favorable conditions for product formation through enhanced availability of major sucrose precursors (glucose-1-phosphate and fructose-6-phosphate) and maintained redox balance through multiple mechanisms.

CONCLUSIONS

Our analysis of circadian regulatory rewiring of carbon metabolism and redox balancing suggests two potential approaches that could be developed for improving cyanobacterial bioproduction: leveraging natural circadian rhythms for optimizing cultivation conditions and timing of pathway induction, and engineering strains that mimic circadian-driven metabolic shifts through controlled carbon flux redistribution and redox rebalancing. While these strategies remain to be tested, they could theoretically improve the efficiency of photosynthetic bioproduction by enabling better temporal separation between cell growth, carbon storage accumulation, and product synthesis phases.

Chemical genetic interactions elucidate pathways controlling tuberculosis antibiotic efficacy during infection

Successful tuberculosis therapy requires treatment with an unwieldy multidrug combination for several months. Thus, there is a growing need to identify novel genetic vulnerabilities that can be leveraged to develop new, more effective antitubercular drugs. Consequently, recent efforts to optimize tuberculosis (TB) therapy have exploited Mycobacterium tuberculosis (Mtb) chemical genetics to identify pathways influencing antibiotic efficacy, novel mechanisms of antibiotic action, and new targets for TB drug discovery. However, the influence of the complex host environment on these interactions remains largely unknown, leaving the therapeutic potential of the identified targets unclear. In this study, we leveraged a library of conditional mutants targeting 467 essential Mtb genes to characterize the chemical-genetic interactions (CGIs) with TB drugs directly in the mouse infection model. We found that these in vivo CGIs differ significantly from those identified in vitro. Both drug-specific and drug-agnostic effects were identified, and many were preserved during treatment with a multidrug combination, suggesting numerous strategies for enhancing therapy. This work also elucidated the complex effects of pyrazinamide (PZA), a drug that relies on aspects of the infection environment for efficacy. Specifically, our work supports the importance of coenzyme A synthesis- inhibition during infection, as well as the antagonistic effect of iron limitation on PZA activity. In addition, we found that inhibition of thiamine and purine synthesis increases PZA efficacy, suggesting additional therapeutically exploitable metabolic dependencies. Our findings present a map of the unique in vivo CGIs, characterizing the mechanism of PZA activity in vivo and identifying potential targets for TB drug development.

FGeneBERT: function-driven pre-trained gene language model for metagenomics

Metagenomic data, comprising mixed multi-species genomes, are prevalent in diverse environments like oceans and soils, significantly impacting human health and ecological functions. However, current research relies on K-mer, which limits the capture of structurally and functionally relevant gene contexts. Moreover, these approaches struggle with encoding biologically meaningful genes and fail to address the one-to-many and many-to-one relationships inherent in metagenomic data. To overcome these challenges, we introduce FGeneBERT, a novel metagenomic pre-trained model that employs a protein-based gene representation as a context-aware and structure-relevant tokenizer. FGeneBERT incorporates masked gene modeling to enhance the understanding of inter-gene contextual relationships and triplet enhanced metagenomic contrastive learning to elucidate gene sequence-function relationships. Pre-trained on over 100 million metagenomic sequences, FGeneBERT demonstrates superior performance on metagenomic datasets at four levels, spanning gene, functional, bacterial, and environmental levels and ranging from 1 to 213 k input sequences. Case studies of ATP synthase and gene operons highlight FGeneBERT's capability for functional recognition and its biological relevance in metagenomic research.

A comparative metabolomics analysis of Açaí (Euterpe oleracea Mart.) fruit, food powder, and botanical dietary supplement extracts

INTRODUCTION

Euterpe oleracea Mart. (açaí) is a botanical of interest to many who seek functional foods that provide antioxidant and anti-inflammatory properties. Cancer patients are increasingly taking botanical dietary supplements containing açaí to complement their conventional therapeutics, which may lead to serious adverse events. Before testing our açaí extracts in vitro for botanical-drug interactions, the goal is to chemically characterize our extracts for compounds whose biological activity in açaí is unknown.

OBJECTIVE

The objective of this work was to develop a chemical fingerprinting method for untargeted characterization of açaí samples from a variety of sources, including food products and botanical dietary supplement capsules, made with multiple extraction solvents.

METHODS

An optimized LC-MS method was generated for in-depth untargeted fingerprinting of chemical constituents in açaí extracts. Statistical analysis models were used to describe relationships between the açaí extracts based on molecular features found in both positive and negative mode ESI.

RESULTS

In an attempt to elucidate the differences in metabolites among açaí extracts from different cultivars, we identified or tentatively identified 173 metabolites from the 16 extracts made from 6 different sources. Of these compounds, there are 138 reported in açaí for the first time. Statistical models showed similar yet distinct differences between the extracts tested based on the polarity of compounds present and the origin of the source material.

CONCLUSION

A high-resolution mass spectrometry method was generated that allowed us to greatly characterize 16 complex extracts made from different sources of açaí with different extraction solvent polarities.

CIFR (Clone-Integrate-Flip-out-Repeat): A toolset for iterative genome and pathway engineering of Gram-negative bacteria

Advanced genome engineering enables precise and customizable modifications of bacterial species, and toolsets that exhibit broad-host compatibility are particularly valued owing to their portability. Tn5 transposon vectors have been widely used to establish random integrations of desired DNA sequences into bacterial genomes. However, the iteration of the procedure remains challenging because of the limited availability and reusability of selection markers. We addressed this challenge with CIFR, a mini-Tn5 integration system tailored for iterative genome engineering. The pCIFR vectors incorporate attP and attB sites flanking an antibiotic resistance marker used to select for the insertion. Subsequent removal of antibiotic determinants is facilitated by the Bxb1 integrase paired to a user-friendly counter-selection marker, both encoded in auxiliary plasmids. CIFR delivers engineered strains harboring stable DNA insertions and free of any antibiotic resistance cassette, allowing for the reusability of the tool. The system was validated in Pseudomonas putida, Escherichia coli, and Cupriavidus necator, underscoring its portability across diverse industrially relevant hosts. The CIFR toolbox was calibrated through combinatorial integrations of chromoprotein genes in P. putida, generating strains displaying a diverse color palette. We also introduced a carotenoid biosynthesis pathway in P. putida in a two-step engineering process, showcasing the potential of the tool for pathway balancing. The broad utility of the CIFR toolbox expands the toolkit for metabolic engineering, allowing for the construction of complex phenotypes while opening new possibilities in bacterial genetic manipulations.

Bifidobacterium adolescentis-derived nicotinic acid improves host skeletal muscle mitochondrial function to ameliorate sarcopenia

Sarcopenia significantly diminishes quality of life and increases mortality risk in older adults. While the connection between the gut microbiome and muscle health is recognized, the underlying mechanisms are poorly understood. In this study, shotgun metagenomics revealed that Bifidobacterium adolescentis is notably depleted in individuals with sarcopenia, correlating with reduced muscle mass and function. This finding was validated in aged mice. Metabolomics analysis identified nicotinic acid as a key metabolite produced by B. adolescentis, linked to improvements in muscle mass and functionality in individuals with sarcopenia. Mechanistically, nicotinic acid restores nicotinamide adenine dinucleotide (NAD+) levels in muscle, inhibits the FoxO3/Atrogin-1/Murf-1 axis, and promotes satellite cell proliferation, reducing muscle atrophy. Additionally, NAD+ activation enhances the silent-information-regulator 1 (SIRT1)/peroxisome-proliferator-activated-receptor-γ-coactivator 1-alpha (PGC-1α) axis, stimulating mitochondrial biogenesis and promoting oxidative metabolism in slow-twitch fibers, ultimately improving muscle function. Our findings suggest that B. adolescentis-derived nicotinic acid could be a promising therapeutic strategy for individuals with sarcopenia.

Complex system modelling reveals oxalate homeostasis is driven by diverse oxalate-degrading bacteria

Decades of research have made clear that host-associated microbiomes touch all facets of health. However, effective therapies that target the microbiome have been elusive given its inherent complexity. Here, we experimentally examined diet-microbe-host interactions through a complex systems framework, centered on dietary oxalate. Using multiple, independent molecular, animal, and in vitro experimental models, we found that microbiome composition influenced multiple oxalate-microbe-host interfaces. Importantly, administration of the oxalate-degrading specialist, Oxalobacter formigenes, was only effective against a poor oxalate-degrading microbiota background and gives critical new insights into why clinical intervention trials with this species exhibit variable outcomes. Data suggest that, while heterogeneity in the microbiome impacts multiple diet-host-microbe interfaces, metabolic redundancy among diverse microorganisms in specific diet-microbe axes is a critical variable that may impact the efficacy of bacteriotherapies, which can help guide patient and probiotic selection criteria in probiotic clinical trials.

Comparative analysis of Legionella lytica genome identifies specific metabolic traits and virulence factors

The complete genome of Legionella lytica PCM 2298 was sequenced and analyzed to provide insights into its genomic structure, virulence potential, and evolutionary position within the Legionella genus. The genome comprised a 3.2 Mbp chromosome and two plasmids, pLlyPCM2298_1 and pLlyPCM2298_2, contributing to a total genome size of 3.7 Mbp. Functional annotation identified 3,165 coding sequences, including genes associated with known virulence factors such as the major outer membrane protein (MOMP), the macrophage infectivity potentiator (Mip), and a comprehensive set of secretion systems (type II, type IVA, and type IVB Dot/Icm type IV secretion system). Notably, L. lytica contributed 383 unique genes to the Legionella pangenome, with 232 identified effector proteins, of which 35 were plasmid-encoded. The identification of unique genes, particularly those on plasmids, suggests an evolutionary strategy favoring horizontal gene transfer and niche adaptation. The effector repertoire included proteins with domains characteristic of host interaction strategies, such as ankyrin repeats and protein kinases. Comparative analyses showed that while L. lytica shares core virulence traits with other Legionella species, it has distinct features that may contribute to its adaptability and pathogenic potential. These findings underscore the genomic diversity within the genus and contribute to a deeper understanding of Legionella's ecological and clinical significance. A custom web application was developed using the R Shiny library, enabling users to interactively explore the expanded Legionella pangenome through UpSet plots.

The role and application of bioinformatics techniques and tools in drug discovery

The process of drug discovery and development is both lengthy and intricate, demanding a substantial investment of time and financial resources. Bioinformatics techniques and tools can not only accelerate the identification of drug targets and the screening and refinement of drug candidates, but also facilitate the characterization of side effects and the prediction of drug resistance. High-throughput data from genomics, transcriptomics, proteomics, and metabolomics make significant contributions to mechanics-based drug discovery and drug reuse. This paper summarizes bioinformatics technologies and tools in drug research and development and their roles and applications in drug research and development, aiming to provide references for the development of new drugs and the realization of precision medicine.

Use of fecal microbiome to understand the impact of housing conditions on metabolic stress responses in farmed saltwater crocodiles (Crocodylus porosus)

Introduction

Understanding the impact of housing conditions on the stress responses in farmed saltwater crocodiles (Crocodylus porosus) is crucial for optimizing welfare and management practices.

Methods

This study employed a multi-omics methodology, combining targeted and untargeted LC-MS for metabolite, lipid, and hormone profiling with 16S rRNA gene sequencing for microbiome analysis, to compare stress responses and changes in fecal samples of crocodiles housed in single versus group pens. Metabolic responses to a startle test were evaluated through multivariate analysis, and changes post-stress were examined.

Results

A total of 564 metabolic features were identified. Of these, 15 metabolites were linked to the cortisol biosynthesis pathway. Metabolite origin analysis showed that 128 metabolites originated from the host, 151 from the microbiota, and 400 remained unmatched. No significant differences in fecal corticosterone levels were observed between single and group pens. However, metabolic profiling revealed distinct differences in stress responses: single pen crocodiles exhibited downregulation of certain compounds and upregulation of others, affecting pyrimidine and purine metabolism pathways when compared to grouped pen crocodiles, linked to altering energy associated induced stress. Additionally, fecal microbiome analysis indicated increased Firmicutes:Bacteroides (F:B) ratio in group-housed animals, suggesting greater stress.

Discussion

The study highlights that while traditional stress indicators like corticosterone levels may not differ significantly between housing conditions, metabolic and microbiome analyses provide deeper insights into stress responses. Single pens are associated with less metabolic disruption and potentially better health outcomes compared to group pens. These findings underscore the value of fecal microbiome and metabolomics in assessing animal welfare in farmed crocodiles.

Metabolomics Profiling and Advanced Methodologies for Wheat Stress Research

Metabolomics is an omics technology that studies the types, quantities, and changes of endogenous metabolic substances in organisms affected by abiotic and biotic factors.

BACKGROUND/OBJECTIVES

Based on metabolomics, small molecule metabolites in biological organisms can be qualitatively and quantitatively analysed. This method analysis directly correlates with biological phenotypes, facilitating the interpretation of life conditions. Wheat (Triticum aestivum L.) is one of the major food crops in the world, and its quality and yield play important roles in safeguarding food security.

METHODS

This review elaborated on the significance of metabolomics research techniques and methods in enhancing wheat resilience against biotic and abiotic stresses.

RESULTS

Metabolomics plays an important role in identifying the metabolites in wheat that respond to diverse stresses. The integrated examination of metabolomics with other omics disciplines provides new insights and approaches for exploring resistance genes, understanding the genetic basis of wheat metabolism, and revealing the mechanisms involved in stress responses.

CONCLUSIONS

Emerging metabolomics research techniques to propose innovative avenues of research is important to enhance wheat resistance.

A phosphorylation signal activates genome-wide transcriptional control by BfmR, the global regulator of Acinetobacter resistance and virulence

The nosocomial pathogen Acinetobacter baumannii is a major threat to human health. The sensor kinase-response regulator system, BfmS-BfmR, is essential to multidrug resistance and virulence in the bacterium and represents a potential antimicrobial target. Important questions remain about how the system controls resistance and pathogenesis. Although BfmR knockout alters expression of >1000 genes, its direct regulon is undefined. Moreover, how phosphorylation controls the regulator is unclear. Here, we address these problems by combining mutagenesis, ChIP-seq, and in vitro phosphorylation to study the functions of phospho-BfmR. We show that phosphorylation is required for BfmR-mediated gene regulation, antibiotic resistance, and sepsis development in vivo. Consistent with activating the protein, phosphorylation induces dimerization and target DNA affinity. Integrated analysis of genome-wide binding and transcriptional profiles of BfmR led to additional key findings: (1) Phosphorylation dramatically expands the number of genomic sites BfmR binds; (2) DNA recognition involves a direct repeat motif widespread across promoters; (3) BfmR directly regulates 303 genes as activator (e.g., capsule, peptidoglycan, and outer membrane biogenesis) or repressor (pilus biogenesis); (4) BfmR controls several non-coding sRNAs. These studies reveal the centrality of a phosphorylation signal in driving A. baumannii disease and disentangle the extensive pathogenic gene-regulatory network under its control.

Metadag: a web tool to generate and analyse metabolic networks

BACKGROUND

MetaDAG is a web-based tool developed to address challenges posed by big data from omics technologies, particularly in metabolic network reconstruction and analysis. The tool is capable of constructing metabolic networks for specific organisms, sets of organisms, reactions, enzymes, or KEGG Orthology (KO) identifiers. By retrieving data from the KEGG database, MetaDAG helps users visualize and analyze complex metabolic interactions efficiently.

RESULTS

MetaDAG computes two models: a reaction graph and a metabolic directed acyclic graph (m-DAG). The reaction graph represents reactions as nodes and metabolite flow between them as edges. The m-DAG simplifies the reaction graph by collapsing strongly connected components, significantly reducing the number of nodes while maintaining connectivity. MetaDAG can generate metabolic networks from various inputs, including KEGG organisms or custom data (e.g., reactions, enzymes, KOs). The tool displays these models on an interactive web page and provides downloadable files, including network visualizations. MetaDAG was tested using two datasets. In an eukaryotic analysis, it successfully classified organisms from the KEGG database at the kingdom and phylum levels. In a microbiome study, MetaDAG accurately distinguished between Western and Korean diets and categorized individuals by weight loss outcomes based on dietary interventions.

CONCLUSION

MetaDAG offers an effective and versatile solution for metabolic network reconstruction from diverse data sources, enabling large-scale biological comparisons. Its ability to generate synthetic metabolisms and its broad application, from taxonomy classification to diet analysis, make it a valuable tool for biological research. MetaDAG is available online, with user support provided via a comprehensive guide. MetaDAG: https://bioinfo.uib.es/metadag/ User guide: https://biocom-uib.github.io/MetaDag/.

ChIP-mini: a low-input ChIP-exo protocol for elucidating DNA-binding protein dynamics in intracellular pathogens

Genome-wide identification of binding profiles for DNA-binding proteins from the limited number of intracellular pathogens in infection studies is crucial for understanding virulence and cellular processes but remains challenging, as the current ChIP-exo is designed for high-input bacterial cells (>1010). Here, we developed an optimized ChIP-mini method, a low-input ChIP-exo utilizing a 5,000-fold reduced number of initial bacterial cells and an analysis pipeline, to identify genome-wide binding dynamics of DNA-binding proteins in host-infected pathogens. Applying ChIP-mini to intracellular Salmonella Typhimurium, we identified 642 and 1,837 binding sites of H-NS and RpoD, respectively, elucidating changes in their binding position and binding intensity during infection. Post-infection, we observed 21 significant reductions in H-NS binding at intergenic regions, exposing the promoter region of virulence genes, such as those in Salmonella pathogenicity islands-2, 3 and effectors. Furthermore, we revealed the crucial phenomenon that novel and significantly increased RpoD bindings were found within regions exhibiting diminished H-NS binding, thereby facilitating substantial upregulation of virulence genes. These findings markedly enhance our understanding of how H-NS and RpoD simultaneously coordinate the transcription initiation of virulence genes within macrophages. Collectively, this work demonstrates a broadly adaptable tool that will enable the elucidation of DNA-binding protein dynamics in diverse intracellular pathogens during infection.

A noncanonical polyamine from bacteria antagonizes animal mitochondrial function

Canonical polyamines such as agmatine, putrescine, and spermidine are evolutionarily conserved metabolites found in nearly all forms of life ranging from bacteria to humans. Recently, interactions between polyamines produced by gut bacteria and human intestinal cells have been proposed to contribute to both Irritable Bowel Syndrome with Diarrhea (IBS-D) and inflammatory bowel diseases. However, the molecular mechanisms that underlie these effects are often unclear due in part to limitations in the methods used to manipulate and study polyamine functions in vivo. Here, we developed a Caenorhabditis elegans based screening platform and a modified LC-MS approach for profiling polyamine metabolites. We combined these methods to make the unexpected discovery that dysfunctional polyamine metabolism in both Gram-negative (E. coli) and Gram-positive (B. subtilis) bacteria can result in the accumulation of a noncanonical polyamine intermediate, N1-Aminopropylagmatine (N1-APA). We further find that N1-APA is produced via spermidine synthase (SpeE) and that it is bioactive when encountered by animals. Specifically, we find that when N1-APA is produced by bacteria in animal intestines it can be transported into intestinal cells via the polyamine transporter CATP-5 where it antagonizes both animal development and mitochondrial function across diverse animal species. Lastly, we find that N1-APA functions analogously to the deoxyhypusine synthase inhibitor GC7. For example, like GC7, N1-APA antagonizes eIF5A hypusination and inhibits the alternative activation of mammalian macrophages. To our knowledge, these findings are the first to demonstrate that N1-APA is a bioactive metabolite and that bacteria can produce a small molecule that functions similarly to existing deoxyhypusine synthase inhibitors. Furthermore, these results suggest an exciting new mechanistic hypothesis for why the loss of speB in gut microbes, including E. coli, has been both linked to inflammatory bowel disease (IBD) in humans and found to drive IBD in germ free mice.

Identification of two glycosyltransferases required for synthesis of membrane glycolipids in Clostridioides difficile

Clostridioides difficile infections cause over 12,000 deaths and an estimated one billion dollars in healthcare costs annually in the United States. The cell membrane is an essential structure that is important for protection from the extracellular environment, signal transduction, and transport of nutrients. The polar membrane lipids of C. difficile are ~50% glycolipids, a higher percentage than most other organisms. The glycolipids of C. difficile consist of monohexosyldiradylglycerol (MHDRG) (~14%), dihexosyldiradylglycerol (DHDRG) (~15%), trihexosyldiradylglycerol (THDRG) (~5%), and a unique glycolipid aminohexosyl-hexosyldiradylglycerol (HNHDRG) (~16%). Previously, we found HexSDF are required for synthesis of HNHDRG. The enzymes required for synthesis of MHDRG, DHDRG, and THDRG are not known. In this study, we identified the glycosyltransferases UgtA (CDR20291_0008), which is required for synthesis of all glycolipids, and UgtB (CDR20291_1186), which is required for synthesis of DHDRG and THDRG. We propose a model where UgtA synthesizes only MHDRG, HexSDF synthesize HNHDRG from MHDRG, and UgtB synthesizes DHDRG and potentially THDRG from MHDRG. We also report that glycolipids are important for critical cell functions, including sporulation, cell size and morphology, maintaining membrane fluidity, colony morphology, and resistance to some membrane targeting antimicrobials.

Domainator, a flexible software suite for domain-based annotation and neighborhood analysis, identifies proteins involved in antiviral systems

The availability of large databases of biological sequences presents an opportunity for in-depth exploration of gene diversity and function. Bacterial defense systems are a rich source of diverse but difficult to annotate genes with biotechnological applications. In this work, we present Domainator, a flexible and modular software suite for domain-based gene neighborhood and protein search, extraction and clustering. We demonstrate the utility of Domainator through three examples related to bacterial defense systems. First, we cluster CRISPR-associated Rossman fold (CARF) containing proteins with difficult to annotate effector domains, classifying most of them as likely transcriptional regulators and a subset as likely RNases. Second, we extract and cluster P4-like phage satellite defense hotspots, identify an abundant variant of Lamassu defense systems and demonstrate its in vivo activity against several T-even phages. Third, we integrate a protein language model into Domainator and use it to identify restriction endonucleases with low similarity to known reference sequences, validating the activity of one example in vitro. Domainator is made available as an open-source package with detailed documentation and usage examples.

PTMNavigator: interactive visualization of differentially regulated post-translational modifications in cellular signaling pathways

Post-translational modifications (PTMs) play pivotal roles in regulating cellular signaling, fine-tuning protein function, and orchestrating complex biological processes. Despite their importance, the lack of comprehensive tools for studying PTMs from a pathway-centric perspective has limited our ability to understand how PTMs modulate cellular pathways on a molecular level. Here, we present PTMNavigator, a tool integrated into the ProteomicsDB platform that offers an interactive interface for researchers to overlay experimental PTM data with pathway diagrams. PTMNavigator provides ~3000 canonical pathways from manually curated databases, enabling users to modify and create custom diagrams tailored to their data. Additionally, PTMNavigator automatically runs kinase and pathway enrichment algorithms whose results are directly integrated into the visualization. This offers a comprehensive view of the intricate relationship between PTMs and signaling pathways. We demonstrate the utility of PTMNavigator by applying it to two phosphoproteomics datasets, showing how it can enhance pathway enrichment analysis, visualize how drug treatments result in a discernable flow of PTM-driven signaling, and aid in proposing extensions to existing pathways. By enhancing our understanding of cellular signaling dynamics and facilitating the discovery of PTM-pathway interactions, PTMNavigator advances our knowledge of PTM biology and its implications in health and disease.

iModulonDB 2.0: dynamic tools to facilitate knowledge-mining and user-enabled analyses of curated transcriptomic datasets

iModulons-sets of co-expressed genes identified through independent component analysis (ICA) of high-quality transcriptomic datasets-provide an unbiased, modular view of an organism's transcriptional regulatory network. Established in 2020, iModulonDB (iModulonDB.org) serves as a centralized repository of curated iModulon sets, enabling users to explore iModulons and download the associated transcriptomic data. This update reflects a significant expansion of the database-19 new ICA decompositions (+633%) spanning 8 925 expression profiles (+1370%), 503 studies (+2290%) and 12 additional organisms (+400%)-and introduces new features to help scientists decipher the mechanisms governing prokaryotic transcriptional regulation. To facilitate comprehension of the underlying expression profiles, the updated user-interface displays essential information about each data-generating study (e.g. the experimental conditions and publication abstract). Dashboards now include condition-specific coloring and highlight data generated from genetically perturbed strains, enabling users to rapidly interpret disruptions in transcriptional regulation. New interactive graphs rapidly convey omics-derived indicators (e.g. the explained variance of ICA decompositions, genetic overlap between iModulons and regulons). Direct links to operon diagrams (BioCyc) and protein-protein interaction networks (STRING) provide users with seamless access to external resources for further assessment of iModulons. Lastly, a new suite of search-driven and species-wide analysis tools promotes user-engagement with iModulons, reinforcing iModulonDB's role as a dynamic, interactive knowledgebase of prokaryotic transcriptional regulation.

Carbon dioxide enhances Akkermansia muciniphila fitness and anti-obesity efficacy in high-fat diet mice

Numerous studies and clinical applications have underscored the therapeutic potential of the indigenous gut bacterium Akkermansia muciniphila in various diseases. However, our understanding of how Akkermansia muciniphila senses and responds to host gastrointestinal signals remains limited. Here, we demonstrate that A. muciniphila exhibits rapid growth, facilitated by its self-produced carbon dioxide (CO₂), with key enzymes such as glutamate decarboxylase, carbonic anhydrase, and pyruvate ferredoxin oxidoreductase playing pivotal roles. Additionally, we design a novel delivery system, comprising calcium carbonate, inulin, A. muciniphila, and sodium alginate, which enhances A. muciniphila growth and facilitates the expression of part probiotic genes in mice intestinal milieu. Notably, the administration of this delivery system induces weight loss in mice fed high-fat diets. Furthermore, we elucidate the significant impact of CO₂ on the composition and functional genes of the human gut microbiota, with genes encoding carbonic anhydrase and amino acid metabolism enzymes exhibiting heightened responsiveness. These findings reveal a novel mechanism by which gut commensal bacteria sense and respond to gaseous molecules, thereby promoting growth. Moreover, they suggest the potential for designing rational therapeutic strategies utilizing live bacterial delivery systems to enhance probiotic growth and ameliorate gut microbiota-related diseases.